Name: 第01講--學習問題 (Lecture 01 - The Learning Problem)
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基礎被動語態

ANNOUNCER: The following program is brought to you by Caltech.

YASER ABU-MOSTAFA: Welcome to machine learning, and welcome to our online

Let me start with an outline of the course, and then go into the material

As you see from the outline, the topics are given colors, and that

designates their main content, whether it's mathematical or practical.

Machine learning is a very broad subject.

It goes from very abstract theory to extreme practice as in rules of thumb.

And the inclusion of a topic in the course depends on the relevance to

So some mathematics is useful because it gives you the conceptual framework,

and then some practical aspects are useful because they give you the way

Now if you look at the topics, these are not meant to be separate topics

They just highlight the main content of those lectures.

But there is a story line that goes through it, and let me tell you what

There is a logical dependency that goes through the course, and there's

one exception to that logical dependency.

One lecture, which is the third one, doesn't really belong here.

It's a practical topic, and the reason I included it early on is because I

needed to give you some tools to play around with, to test the

If I waited until it belonged normally, which is to the second aspect of the

linear models which is down there, the beginning of the course would be

And as you see, if you look at the colors, it is mostly red in the

So it starts building the concepts and the theory.

And then it goes on to the practical aspects.

And the subject of the lecture is the learning problem.

It's an introduction to what learning is.

And I will draw your attention to one aspect of this slide,

And believe it or not, this is not artwork.

This is actually a technical figure that will come up

So you can wait in anticipation until it comes up, but this will actually be

a scientific figure that we will talk about.

Now when we move to today's lecture, I'm going to talk

Machine learning is a very broad subject, and I'm going to start with

one example that captures the essence of machine learning.

It's a fun example about movies that everybody watches.

And then after that, I'm going to abstract from the learning problem,

the practical learning problem, aspects that are common to all

learning situations that you're going to face.

And in abstracting them, we'll have the mathematical formalization of the

And then we will get our first algorithm for machine learning today.

It's a very simple algorithm, but it will fix the idea about what is the

And we will survey the types of learning, so that we know which part we

are emphasizing in this course, and which parts are nearby.

And I will end up with a puzzle, a very interesting puzzle, and it's

a puzzle in more ways than one, as you will see.

The example of machine learning that I'm going to start with is how

Now that is an interesting problem, and it's interesting for us because we

watch movies, and very interesting for a company that rents out movies.

And indeed, a company which is Netflix wanted to improve the in-house system

So they make recommendations when you log in, they recommend movies that

they think you will like, so they think that you'll rate them highly.

And they had a system, and they wanted to improve the system.

So how much is a 10% improvement in performance worth to the company?

It was actually $1 million that was paid out to the first group that

actually managed to get the 10% improvement.

So you ask yourself, 10% improvement in something like that, why should

It's because, if the recommendations that the movie company makes are spot

on, you will pay more attention to the recommendation, you are likely to rent

the movies that they recommend, and they will make lots of money-- much

more than the million dollars they promised.

And this is very typical in machine learning.

For example, machine learning has applications in financial forecasting.

You can imagine that the minutest improvement in financial forecasting

So the fact that you can actually push the system to be better using machine

learning is a very attractive aspect of the technique in a wide spectrum of

They gave the data, and people started working on the problem using different

algorithms, until someone managed to get the prize.

Now if you look at the problem of rating a movie, it captures the

essence of machine learning, and the essence has three components.

If you find these three components in a problem you have in your field, then

you know that machine learning is ready as an application tool.

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第01講--學習問題 (Lecture 01 - The Learning Problem)

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